Gestion flexible des ressources dans les réseaux de nouvelle génération avec SDN

Abstract : 5G and beyond-5G/6G are expected to shape the future economic growth of multiple vertical industries by providing the network infrastructure required to enable innovation and new business models. They have the potential to offer a wide spectrum of services, namely higher data rates, ultra-low latency, and high reliability. To achieve their promises, 5G and beyond-5G/6G rely on software-defined networking (SDN), edge computing, and radio access network (RAN) slicing technologies. In this thesis, we aim to use SDN as a key enabler to enhance resource management in next-generation networks. SDN allows programmable management of edge computing resources and dynamic orchestration of RAN slicing. However, achieving efficient performance based on SDN capabilities is a challenging task due to the permanent fluctuations of traffic in next-generation networks and the diversified quality of service requirements of emerging applications. Toward our objective, we address the load balancing problem in distributed SDN architectures, and we optimize the RAN slicing of communication and computation resources in the edge of the network. In the first part of this thesis, we present a proactive approach to balance the load in a distributed SDN control plane using the data plane component migration mechanism. First, we propose prediction models that forecast the load of SDN controllers in the long term. By using these models, we can preemptively detect whether the load will be unbalanced in the control plane and, thus, schedule migration operations in advance. Second, we improve the migration operation performance by optimizing the tradeoff between a load balancing factor and the cost of migration operations. This proactive load balancing approach not only avoids SDN controllers from being overloaded, but also allows a judicious selection of which data plane component should be migrated and where the migration should happen. In the second part of this thesis, we propose two RAN slicing schemes that efficiently allocate the communication and the computation resources in the edge of the network. The first RAN slicing scheme performs the allocation of radio resource blocks (RBs) to end-users in two time-scales, namely in a large time-scale and in a small time-scale. In the large time-scale, an SDN controller allocates to each base station a number of RBs from a shared radio RBs pool, according to its requirements in terms of delay and data rate. In the short time-scale, each base station assigns its available resources to its end-users and requests, if needed, additional resources from adjacent base stations. The second RAN slicing scheme jointly allocates the RBs and computation resources available in edge computing servers based on an open RAN architecture. We develop, for the proposed RAN slicing schemes, reinforcement learning and deep reinforcement learning algorithms to dynamically allocate RAN resources.La 5G et au-delà de la 5G/6G sont censées dessiner la future croissance économique de multiples industries verticales en fournissant l'infrastructure réseau nécessaire pour permettre l'innovation et la création de nouveaux modèles économiques. Elles permettent d'offrir un large spectre de services, à savoir des débits de données plus élevés, une latence ultra-faible et une fiabilité élevée. Pour tenir leurs promesses, la 5G et au-delà de la-5G/6G s'appuient sur le réseau défini par logiciel (SDN), l’informatique en périphérie et le découpage du réseau d'accès (RAN). Dans cette thèse, nous visons à utiliser le SDN en tant qu'outil clé pour améliorer la gestion des ressources dans les réseaux de nouvelle génération. Le SDN permet une gestion programmable des ressources informatiques en périphérie et une orchestration dynamique de découpage du RAN. Cependant, atteindre une performance efficace en se basant sur le SDN est une tâche difficile due aux fluctuations permanentes du trafic dans les réseaux de nouvelle génération et aux exigences de qualité de service diversifiées des applications émergentes. Pour atteindre notre objectif, nous abordons le problème de l'équilibrage de charge dans les architectures SDN distribuées, et nous optimisons le découpage du RAN des ressources de communication et de calcul à la périphérie du réseau. Dans la première partie de cette thèse, nous présentons une approche proactive pour équilibrer la charge dans un plan de contrôle SDN distribué en utilisant le mécanisme de migration des composants du plan de données. Tout d'abord, nous proposons des modèles pour prédire la charge des contrôleurs SDN à long terme. En utilisant ces modèles, nous pouvons détecter de manière préemptive si la charge sera déséquilibrée dans le plan de contrôle et, ainsi, programmer des opérations de migration à l'avance. Ensuite, nous améliorons les performances des opérations de migration en optimisant le compromis entre un facteur d'équilibrage de charge et le coût des opérations de migration. Cette approche proactive d'équilibrage de charge permet non seulement d'éviter la surcharge des contrôleurs SDN, mais aussi de choisir judicieusement le composant du plan de données à migrer et l'endroit où la migration devrait avoir lieu. Dans la deuxième partie de cette thèse, nous proposons deux mécanismes de découpage du RAN qui allouent efficacement les ressources de communication et de calcul à la périphérie des réseaux. Le premier mécanisme de découpage du RAN effectue l'allocation des blocs de ressources radio (RBs) aux utilisateurs finaux en deux échelles de temps, à savoir dans une échelle de temps large et dans une échelle de temps courte. Dans l’échelle de temps large, un contrôleur SDN attribue à chaque station de base un certain nombre de RB à partir d'un pool de RB radio partagé, en fonction de ses besoins en termes de délai et de débit. Dans l’échelle de temps courte, chaque station de base attribue ses ressources disponibles à ses utilisateurs finaux et demande, si nécessaire, des ressources supplémentaires aux stations de base adjacentes. Le deuxième mécanisme de découpage du RAN alloue conjointement les RB et les ressources de calcul disponibles dans les serveurs de l’informatique en périphérie en se basant sur une architecture RAN ouverte. Nous développons, pour les mécanismes de découpage du RAN proposés, des algorithmes d'apprentissage par renforcement et d'apprentissage par renforcement profond pour allouer dynamiquement les ressources du RAN

Security Games with Information Leakage: Modeling and Computation

Most models of Stackelberg security games assume that the attacker only knows the defender's mixed strategy, but is not able to observe (even partially) the instantiated pure strategy. Such partial observation of the deployed pure strategy -- an issue we refer to as information leakage -- is a significant concern in practical applications. While previous research on patrolling games has considered the attacker's real-time surveillance, our settings, therefore models and techniques, are fundamentally different. More specifically, after describing the information leakage model, we start with an LP formulation to compute the defender's optimal strategy in the presence of leakage. Perhaps surprisingly, we show that a key subproblem to solve this LP (more precisely, the defender oracle) is NP-hard even for the simplest of security game models. We then approach the problem from three possible directions: efficient algorithms for restricted cases, approximation algorithms, and heuristic algorithms for sampling that improves upon the status quo. Our experiments confirm the necessity of handling information leakage and the advantage of our algorithms

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed

Resource Allocation Among Agents with MDP-Induced Preferences

Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size

04231 Abstracts Collection -- Scheduling in Computer and Manufacturing Systems

During 31.05.-04.06.04, the Dagstuhl Seminar 04231 "Scheduling in Computer and Manufacturing Systems" was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Time Minimization and Online Synchronization for Multi-agent Systems under Collaborative Temporal Tasks

Multi-agent systems can be extremely efficient when solving a team-wide task in a concurrent manner. However, without proper synchronization, the correctness of the combined behavior is hard to guarantee, such as to follow a specific ordering of sub-tasks or to perform a simultaneous collaboration. This work addresses the minimum-time task planning problem for multi-agent systems under complex global tasks stated as Linear Temporal Logic (LTL) formulas. These tasks include the temporal and spatial requirements on both independent local actions and direct sub-team collaborations. The proposed solution is an anytime algorithm that combines the partial-ordering analysis of the underlying task automaton for task decomposition, and the branch and bound (BnB) search method for task assignment. Analyses of its soundness, completeness and optimality as the minimal completion time are provided. It is also shown that a feasible and near-optimal solution is quickly reached while the search continues within the time budget. Furthermore, to handle fluctuations in task duration and agent failures during online execution, an adaptation algorithm is proposed to synchronize execution status and re-assign unfinished subtasks dynamically to maintain correctness and optimality. Both algorithms are validated rigorously over large-scale systems via numerical simulations and hardware experiments, against several strong baselines.Comment: 17 pages, 14 figure

Coalition Formation and Execution in Multi-robot Tasks

In this research, I explore several related problems in distributed robot systems that must be addressed in order to achieve multi-robot tasks, in which individual robots may not possess all the required capabilities. While most previous research work on multi-robot cooperation mainly concentrates on loosely-coupled multi-robot tasks, a more challenging problem is to also address tightly-coupled multi- robot tasks involving close robot interactions, which often require capability sharing. Three related topics towards addressing these tasks are discussed, as follows: Forming coalitions, which determines how robots should form into subgroups (i.e., coalitions) to address individual tasks. To achieve system autonomy, the ability to identify the feasibility of potential solutions is critical for forming coalitions. A general IQ-ASyMTRe architecture, which is formally proven to be sound and complete in this research, is introduced to incorporate this capability based on the ASyMTRe architecture. Executing coalitions, which coordinates different robots within the same coalition during physical execution to accomplish individual tasks. For executing coalitions, the IQ-ASyMTRe+ approach is presented. An information quality measure is introduced to control the robots to maintain the required constraints for task execution in dynamic environment. Redundancies at sensory and computational levels are utilized to enable execution that is robust to internal and external influences. Task allocation, which optimizes the overall performance of the system when multiple tasks need to be addressed. In this research, this problem is analyzed and the formulation is extended. A new greedy heuristic is introduced, which considers inter-task resource constraints to approximate the influence between different assignments in task allocation. Through combining the above approaches, a framework that achieves system autonomy can be created for addressing multi-robot tasks